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Drones
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UAVs and UGVs Robotics for Emergency Response in a Changing...
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UAVs and UGVs Robotics for Emergency Response in a Changing Climate

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Ecology".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 2275

Special Issue Editors

Dr. Abraham Mejia-Aguilar

E-Mail Website
Guest Editor
Eurac Research, Viale Druso 1, 39100 Bolzano, Italy
Interests: UAV; instrumentation; sensors
Dr. Santos Miguel Orozco Soto

E-Mail Website
Guest Editor
Faculty of Engineering, Freie Universität Bozen, 39100 Bolzano, Italy
Interests: aerial manipulator; humanoid robot; control strategies for electrical energy generation systems
Dr. Christian Wankmüller

E-Mail Website
Guest Editor
Department of Operations, Energy, and Environmental Management, Universitaet Klagenfurt, 9020 Klagenfurt am Wörthersee, Austria
Interests: drones and rescue operations; humanitarian logistics

Special Issue Information

Dear Colleagues,

As climate change accelerates the frequency and intensity of natural and human-made disasters—wildfires, floods, landslides, industrial accidents—Europe faces mounting pressure to protect both its citizens and the first responders who risk their lives in emergencies. This Special Issue focuses on the emerging role of ground-based, aerial, or hybrid robotic platforms in safeguarding lives during high-risk operations.

Autonomous and semi-autonomous Unmanned Ground Vehicles (UGVs), often working in swarm configuration with Unmanned Aerial Vehicles (UAVs), are increasingly equipped to operate in degraded, unpredictable, and hazardous environments. These platforms can augment human skills, extend perception and reach, and take action in situations that are too dangerous or time-critical for people alone. From navigating collapsed structures to delivering medical payloads or sensing toxic conditions, such robotic systems have the potential to revolutionize civil protection efforts.

We invite contributions that explore innovations enabling rapid deployment, coordination, and resilience of unmanned vehicle systems under extreme conditions. This includes technological advances, but also systems thinking, field validation, and human-machine integration to ensure real-world applicability.

Topics of interest include, but are not limited to:

  • Swarm and collaborative concepts for First responders and Risk management
  • Autonomous navigation in complex terrains
  • Decision-support system tools for human-robot teaming
  • UGV-based perception, manipulation, and terrain adaptation
  • Real-time UAV-UGV coordination in multi-threat scenarios
  • Deployable robotics for firefighting, flood monitoring, search and rescue, and hazardous materials assessment
  • Ethical design, operational integration, and trust in robotic first responders
  • Climate-driven risk scenarios and the role of robotics in emergency resilience

This Special Issue aims to sensitize researchers, policymakers, and civil protection actors to the urgency of robotic innovation in emergency response—toward a Europe that is safer, faster, and more resilient in the face of environmental extremes.

Dr. Abraham Mejia-Aguilar
Dr. Santos Miguel Orozco Soto
Dr. Christian Wankmüller
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • unmanned aerial vehicles (UAVs)
  • unmanned ground vehicles (UGVs)
  • robotics for hazardous environments
  • disaster robotics
  • human-robot teaming
  • search and rescue (SAR)
  • emergency response automation
  • autonomous navigation
  • real-time situational awareness

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

24 pages, 4159 KB  
Article
A UAV–Satellite Hybrid Pipeline for Wildfire Detection and Dynamic Perimeter Prediction
by Hossein Keshmiri and Khan A. Wahid
Drones 2026, 10(4), 263; https://doi.org/10.3390/drones10040263 - 4 Apr 2026
Viewed by 823
Abstract
Effective wildfire management demands seamless integration of real-time detection and long-term spread forecasting. This paper proposes a novel power-efficient UAV–satellite hybrid pipeline that synergizes the agility of UAVs with the scale of satellite intelligence. The system begins with a dashboard-guided, multi-UAV detection module [...] Read more.
Effective wildfire management demands seamless integration of real-time detection and long-term spread forecasting. This paper proposes a novel power-efficient UAV–satellite hybrid pipeline that synergizes the agility of UAVs with the scale of satellite intelligence. The system begins with a dashboard-guided, multi-UAV detection module that scores fire likelihood from historical satellite data and enables scalable, energy-efficient deployment with low-latency onboard processing. This aerial component ensures persistent surveillance and reliable ignition detection, supported by a Dual LoRa (Long Range) communication scheme for robust and low-power connectivity. It achieves an F1-score of 97.4% while minimizing power consumption to extend operational flight times. Following detection, the pipeline transitions to a dynamic perimeter-prediction phase utilizing a custom Canadian boreal dataset. We employ a Squeeze-and-Excitation Residual U-Net (SE-ResUNet) to model spatiotemporal fire propagation based on static terrain and dynamic environmental features. The model was validated using a dynamic simulation framework that evaluates temporal consistency and convergence behavior against final cumulative burned-area masks, effectively addressing the absence of daily ground truth. Under these conditions, the model achieves a recall of 84% and an AUC of 0.97, demonstrating a strong capability to delineate active fire fronts. By coupling dashboard-driven UAV sensing with satellite-based predictive modeling, this work establishes a modular, foundational framework to support data-scarce forecasting in modern wildfire management. Full article
(This article belongs to the Special Issue UAVs and UGVs Robotics for Emergency Response in a Changing Climate)
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27 pages, 6535 KB  
Article
Self-Correcting Cascaded Localization to Mitigate Drift in Mining Vehicles’ Kilometer-Scale Travel
by Miao Yu, Zilong Zhang, Xi Zhang, Junjie Zhang and Bin Zhou
Drones 2025, 9(11), 810; https://doi.org/10.3390/drones9110810 - 20 Nov 2025
Viewed by 842
Abstract
High-reliability localization is essential for underground mining autonomous vehicle, as inaccurate positioning triggers collision risks and limits deployment in safety-critical environments. Underground mining localization faces unique challenges: kilometer-scale signal-free tunnels restrict traditional technologies, while wheel slippage-induced non-Gaussian noise and geometric-degraded tunnel localization failures [...] Read more.
High-reliability localization is essential for underground mining autonomous vehicle, as inaccurate positioning triggers collision risks and limits deployment in safety-critical environments. Underground mining localization faces unique challenges: kilometer-scale signal-free tunnels restrict traditional technologies, while wheel slippage-induced non-Gaussian noise and geometric-degraded tunnel localization failures further reduce accuracy—issues existing methods cannot address simultaneously. To resolve these bottlenecks, this study develops a scenario-adapted, self-correcting positioning system for underground autonomous vehicles, fusing multi-source onboard sensor data to suppress slip noise and ensure feature-deficient environment robustness. We propose a three-stage cascaded filtering system: it first fuses LiDAR, IMU, wheel speed, and steering angle data for a self-contained framework, then adds two dedicated modules for core challenges. For wheel slippage noise, an anti-slip prior estimation algorithm integrates kinematic models with IMU data, plus a low-adhesion mine surface-tailored slip compensation mechanism to ensure reliable state estimation and eliminate slip deviations. For geometrically degraded tunnel failures, an anti-degradation algorithm uses point cloud degradation-derived regularization constraints and regularized Kalman filtering to enable stable positioning updates. Experiments show that the system achieves sub-meter accuracy and full-area coverage underground, with improved performance under severe wheel slip and in feature-deprived zones. This work fills the gap in high-reliability, self-contained localization for kilometer-scale underground mining vehicles and provides a safety-oriented paradigm for autonomous vehicle scaling, aligning with critical scenario driving safety demands. Full article
(This article belongs to the Special Issue UAVs and UGVs Robotics for Emergency Response in a Changing Climate)
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